Using spatiotemporal blocks to reduce the uncertainty in detecting and tracking moving objects in video

Authors:
Longin Jan Latecki;Vasileios Megalooikonomou;Roland Miezianko;Dragoljub Pokrajac
Affiliations:
Department of Computer and Information Sciences, Temple University, 3rd floor Wachman Hall, 1805 N. Broad St., Philadelphia PA 19122, USA.;Data Engineering Laboratory (DEnLab), Center for Information Science and Technology, Department of Computer and Information Sciences, Temple University, 3rd floor Wachman Hall, 1805 N. Bro ...;Department of Computer and Information Sciences, Temple University, 3rd floor Wachman Hall, 1805 N. Broad St., Philadelphia PA 19122, USA.;Computer and Information Science Department, Delaware State University, 1200 N Dupont Hwy, Dover, 19901 DE, USA
Venue:
International Journal of Intelligent Systems Technologies and Applications
Year:
2006

Citing 9
Cited 4

Pfinder: Real-Time Tracking of the Human Body

IEEE Transactions on Pattern Analysis and Machine Intelligence
Special Section on Video Surveillance

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Video Compression: Techniques and Algorithms

Real-Time Video Compression: Techniques and Algorithms
Video-Based Surveillance Systems: Computer Vision and Distributed Processing

Video-Based Surveillance Systems: Computer Vision and Distributed Processing
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information

MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

Motion representation using composite energy features

Pattern Recognition
Outlier Detection with Kernel Density Functions

MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A Comparative Study of Outlier Detection Algorithms

MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Incremental connectivity-based outlier factor algorithm

VoCS'08 Proceedings of the 2008 international conference on Visions of Computer Science: BCS International Academic Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a novel method for detecting moving objects in videos. The method represents videos using spatiotemporal blocks instead of pixels. Dimensionality reduction is performed to obtain a compact representation of each block's values. The block vectors provide a joint representation of texture and motion patterns. The motion detection and tracking experiments demonstrate that our method although simpler than a state-of-the-art method based on the Stauffer-Grimson Gaussian mixture model has superior performance. It reduces both the instability and the processing time making real-time processing of high resolution videos and efficient analysis of large scale video data feasible.